Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49)

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IPython 6.1.0 -- An enhanced Interactive Python.


Restarting kernel...




In [1]: runfile('/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes/predict_test.py', wdir='/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes')

CUDA: True

loading saved model

Predicting on Training Images



Question = ['how', 'many', 'slices', 'have', 'been', 'cut?']


Correct Answer = 1

Predicted Answer = 1



Question = ['how', 'many', 'rows', 'of', 'donuts', 'are', 'there', 'on', 'the', 'top', 'shelf?']


Correct Answer = 8

Predicted Answer = 8



Question = ['how', 'many', 'keyboards', 'can', 'be', 'seen?']


Correct Answer = 1

Predicted Answer = 1



Question = ['how', 'many', 'pillows', 'are', 'in', 'the', 'room?']


Correct Answer = 5

Predicted Answer = 6



Question = ['how', 'many', 'people', 'are', 'in', 'the', 'picture?']


Correct Answer = 2

Predicted Answer = 1



Question = ['what', 'color', 'is', 'the', 'sky?']


Correct Answer = gray

Predicted Answer = gray



Question = ['is', 'that', 'zebra', 'making', 'a', 'bowel', 'movement', 'mess', 'on', 'the', 'ground?']


Correct Answer = yes

Predicted Answer = yes



Question = ['how', 'many', 'people', 'are', 'touching', 'a', 'ball?']


Correct Answer = 0

Predicted Answer = 0



Question = ['is', 'this', 'a', 'healthy', 'meal?']


Correct Answer = yes

Predicted Answer = yes



Question = ['what', 'number', 'is', 'the', 'batter?']


Correct Answer = 16

Predicted Answer = 15



Predicting on Test Images



Question = ['where', 'is', 'the', 'woman', 'standing?']


Correct Answer = sidewalk

Predicted Answer = 11:30



Question = ['what', 'fruit', 'is', 'to', 'the', 'right', 'of', 'the', 'avocado?']


Correct Answer = lemons

Predicted Answer = yellow



Question = ['is', 'this', 'outside', 'or', 'in?']


Correct Answer = outside

Predicted Answer = 0



Question = ['is', 'this', 'a', 'large', 'truck?']


Correct Answer = yes

Predicted Answer = yes



Question = ['is', 'this', 'pizza', 'vegetarian?']


Correct Answer = yes

Predicted Answer = eating



Question = ['how', 'many', 'light', 'haired', 'colored', 'people', 'do', 'you', 'see?']


Correct Answer = 0

Predicted Answer = yes



Question = ['what', 'is', 'the', 'percentage', 'of', 'black', 'fur', 'to', 'white', 'fur', 'on', 'the', 'cat?']


Correct Answer = 50

Predicted Answer = 1949



Question = ['is', 'the', 'fridge', 'crowded?']


Correct Answer = yes

Predicted Answer = sandwich



Question = ['where', 'is', 'location?']


Correct Answer = kitchen

Predicted Answer = no



Question = ['how', 'many', 'sides', 'are', 'there', 'to', 'the', 'dish?']


Correct Answer = 3

Predicted Answer = window




In [2]: runfile('/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes/predict_test.py', wdir='/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes')

Reloaded modules: cbow

CUDA: True

loading saved model

Predicting on Training Images



Question = ['what', 'is', 'both', 'orange', 'and', 'red?']


Correct Answer = fire

Predicted Answer = blue



Question = ['where', 'is', 'the', 'sink?']


Correct Answer = kitchen

Predicted Answer = kitchen



Question = ['how', 'many', 'men', 'are', 'in', 'this', 'picture?']


Correct Answer = 2

Predicted Answer = 3



Question = ['is', 'there', 'something', 'on', 'top', 'of', 'the', 'jar?']


Correct Answer = no

Predicted Answer = no



Question = ['is', 'the', 'plane', 'going', 'to', 'land', 'soon?']


Correct Answer = yes

Predicted Answer = yes



Question = ['what', 'color', 'is', 'the', 'elephant?']


Correct Answer = gray

Predicted Answer = white



Question = ['how', 'many', 'animals', 'are', 'in', 'the', 'background?']


Correct Answer = 4

Predicted Answer = 2



Question = ['what', 'is', 'in', 'the', 'glass?']


Correct Answer = orange juice

Predicted Answer = orange juice



Question = ['what', 'color', 'is', 'the', 'goose?']


Correct Answer = gray

Predicted Answer = gray



Question = ['how', 'many', 'motorcycles', 'are', 'there?']


Correct Answer = 1

Predicted Answer = 1



Predicting on Test Images



Question = ['what', 'is', 'the', 'bank', 'on', 'the', 'sign?']


Correct Answer = hsbc

Predicted Answer = white



Question = ['does', 'this', 'look', 'like', 'mother', 'and', 'child?']


Correct Answer = yes

Predicted Answer = elephant



Question = ['how', 'many', 'blades', 'of', 'grass', 'is', 'the', 'giraffe', 'standing', 'on?']


Correct Answer = 100

Predicted Answer = standing



Question = ['is', 'this', 'a', 'cordless', 'mouse?']


Correct Answer = no

Predicted Answer = no



Question = ['how', 'many', 'oranges', 'are', 'there?']


Correct Answer = 3

Predicted Answer = yes



Question = ['is', 'the', 'roof', 'made', 'of', 'tin?']


Correct Answer = no

Predicted Answer = parking lot



Question = ['how', 'many', 'towels', 'are', 'there?']


Correct Answer = 2

Predicted Answer = yes



Question = ['is', 'the', 'street', 'well', 'paved?']


Correct Answer = no

Predicted Answer = airport



Question = ['how', 'many', 'different', 'colors', 'of', 'bananas', 'are', 'there?']


Correct Answer = 3

Predicted Answer = yes



Question = ['how', 'many', 'utensils', 'are', 'in', 'the', 'table?']


Correct Answer = 4

Predicted Answer = pizza




In [3]: